Misleading Failures of Partial-input Baselines

Shi Feng, Eric Wallace, Jordan Boyd-Graber

Abstract: Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e.g., hypothesis-only or image-only models). While the success of a partial-input baseline indicates a dataset is cheatable, our work cautions the converse is not necessarily true. Using artificial datasets, we illustrate how the failure of a partial-input baseline might shadow more trivial patterns that are only visible in the full input. We also identify such artifacts in real natural language inference datasets. Our work provides an alternative view on the use of partial-input baselines in future dataset creation.

Keywords: natural language processing